Inscrição na biblioteca: Guest
Journal of Flow Visualization and Image Processing

Publicou 4 edições por ano

ISSN Imprimir: 1065-3090

ISSN On-line: 1940-4336

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.6 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00013 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.14 SJR: 0.201 SNIP: 0.313 CiteScore™:: 1.2 H-Index: 13

Indexed in

RECOVERING SUBGRID-SCALE FEATURES IN TURBULENT FLOWS THROUGH COMPRESSIVE SENSING

Volume 22, Edição 4, 2015, pp. 199-212
DOI: 10.1615/JFlowVisImageProc.2016017395
Get accessGet access

RESUMO

Compressive sensing is a powerful technique in image processing that can overcome the classical Nyquist criterion in resolving details of the flow. Yet, this has found little applications in thermal-fluid imaging, to our knowledge at this time. We demonstrate that compressive sensing can be used to recover fine-scale features of turbulence, at imaging resolutions far below those thought possible. Several different turbulence geometries and processes are used as examples, and at different sampling geometries and scales. The results show that the pyramidal sampling configuration is by far the most efficient, and also that compressive sensing in general has important applications in sensing of turbulence. In addition, further applications are suggested on resolving subgrid features using compressive sensing.

Portal Digital Begell Biblioteca digital da Begell eBooks Diários Referências e Anais Coleções de pesquisa Políticas de preços e assinaturas Begell House Contato Language English 中文 Русский Português German French Spain